Electric power systems are increasingly influenced by meteorological conditions due the wide penetration of renewable energy plants. In particular, wind speed plays a key role in transmission and distribution systems, as it determines wind power production and it influences line rating; also, extreme values of wind speed (EWS) affect mechanical reliability of wind towers and blades, causing loss of lifetime and damages. Several physical models provide EWS forecasts on global or local scales; however, such models require accurate initial and boundary conditions, and enormous computational effort. Statistical models are good alternatives to achieve accurate forecasts, but they require assumptions on the statistical distribution of EWS in parametric frameworks. This paper proposes an Inverse Burr-Inverse Weibull finite mixture distribution for EWS characterization and its Expectation-Maximization (EM) procedure for parameter estimation. The proposed mixture model is compared to commonly used EWS distributions. Actual sites are considered to evaluate the goodness of fitting in different, real conditions; the EM procedure is compared to the maximum likelihood estimation. Sensitivity and error analyses are performed to individuate the main features of the proposed model; numerical results confirmed the suitability of the proposed model and of the EM estimation in the most of considered cases. (C) 2017 Elsevier Ltd. All rights reserved.